Intelligent Peer-to-Peer Media Streaming

ABSTRACT

An efficient media streaming method utilizing a globally load balanced overlay network. This method makes use of capacity per out-degree values to construct and maintain an overlay network for media streaming in a Peer-to-Peer environment.

BACKGROUND AND SUMMARY OF THE INVENTIONS

The present application relates to Peer-to-Peer (P2P) communications,and more particularly to the cooperation of end hosts in forming overlaytopologies in a P2P networking environment for the distribution ofmultimedia contents, such as bulk files, live or on-demand audio orvideo, etc.

BACKGROUND

Media Streaming

The idea of Peer-to-Peer is by leveraging on the end terminals tocooperatively disseminate the information instead of relying on anycentral entities. Such feature endows the system's theoreticallyinfinite scalability with virtually no additional cost, and thereforehas ignited an explosive growth of large scale Internet services, forinstance, KaZaA for file sharing, BT for bulk content distribution andPPlive for live media streaming.

FIG. 1 shows an example of a general architecture of the peer-to-peerstreaming systems. The original uncompressed video content is generatedby the A/V source and injected into the media server. In the mediaserver, the video content is first compressed with the playout rate tobe r kbps and then is distributed to users in the peer-to-peer networkthrough multiple video streams. In the peer-to-peer network, the videostreams are forwarded among connected nodes, and at each node thedownloading video content is simultaneously played by the local mediaplayer at the playout rate of r kbps.

An appealing feature of the peer-to-peer networking is that since eachparticipating node contributes its bandwidth to the system to assistuploading, the system can self-scale to support a huge number of usersonline simultaneously, even for bandwidth consuming media steamingapplications. Notice that all these attractive services are bandwidthintensive and normally require stringent QoS requirements. On the otherhand, with millions of heterogeneous participants all over the world,how to organize the network efficiently to boost the system performancewith high service quality is really a tough problem. Facing thesedifficulties traditional approaches for live media streaming can mainlybe groped into three categories based on the involvement of (1) amulticast tree, (2) plural multicast trees, or (3) no tree.

The first category is to build a multicast tree across the network.Video contents are disseminated from the root and passed along the treefrom parents to children. Without considering about the dynamic behaviorof peers, this mechanism is superior with its ease to manage.Optimization can also be easily performed within the tree byintelligently shifting among parents and their children. However, withthe intense churns of ad hoc peers, the cost of tree topologymaintenance is high. Further, due to peer departures, the system oftenneeds frequent repairs. Therefore participants will inevitably encounterturbulent QoS with large video quality fluctuation during the repairingphase. Examples of such systems include Narada and Yoid.

To enhance the robustness of the system and allow the clients to enjoyhigh quality services such as smooth playing of videos, the secondcategory improves the first category by building several multicast treessimultaneously with each tree streaming one strip of the content rootedfront a source. Such mechanism often employs the advanced codingschemes, e.g. Multiple Description Coding or Fountain Codes, so that bydownloading several streams simultaneously from different trees, theparticipants are able to achieve better video quality and can alsotolerate the departures of partial predecessors as long as it calldownload at least one stream continuously. Such mechanism has beenadopted in CoopNet SplitStream and Bullet.

In the third category, there are no more tree(s) to be maintained.Instead a data-oriented approach chops the original contents into blocksof equal size and disseminates to peers disjointedly. After downloadingeach block in its entirety, peers can then serve it to other siblings toenhance the system service capacity. In such a scenario, in order tolearn the block propagation to chase needed blocks, each peer needs tocollect the block information of certain amount of peers periodicallyand react against the run up of fresh blocks or other accidents such asneighbor departures, heavy congestion, etc. With the unceasingly activelocal modifications, the network commotion will only affect severalblock sessions of a peer normally and therefore the system is morerobust than that of the previous tree(s) structure. For this reason, thedata driven approach is widerly employed in practice, for instance BT,CoolStream and PPlive. However, such mechanism makes the network evenmore turbulent and difficult to optimize.

With the great success of PPlive and CoolStream, large scale livestreaming service has already been relatively mature. However, the term“streaming” is in fact used loosely and inaccurately. There are nogeneral principles to guide designers seeking foe an optimal system.Large scale Video-on-Demand (VoD) service, with more stringent QoSrequirements and less available cooperation among peers, is farsatisfaction and still under serious research without any real systemimplemented.

To summarize, due to the distinguished characteristics of the P2Pnetworks, efficiently utilizing the overall bandwidth resourceencounters great engineering challenges in the following three aspects.

1. Unpredictable and Dynamically Changing Overall Bandwidth. Let V_(t)denote the set of peers in the network at time t, including the server.Let C_(t) denote the upload capacity of each participating peer i∈V_(t)Then the overall bandwidth available is Σ_(i∈V) _(t) C_(i). Since themedia player requires downloading rate at least r to play the videocontent smoothly, the overall download bandwidth is at least |V_(t)|r,excluding the server. Therefore, it constructing the streaming system, aNecessary Condition Σ_(i∈V) _(t) C_(i)≧(|V_(t)|−1)r. Therefore, thefundamental problem is how to control the system to satisfy thenecessary condition. Solving this problem faces great challenges: First,due to the huge number of dynamic and heterogeneous peers involved inthe network, the overall bandwidth, i.e., Σ_(i∈V) _(t) C_(i), isunpredictable. Second, the average overall bandwidth is changing all thetime because participants are widespread over the world and haveheterogeneous bandwidth capacities. With the asynchronous nature of thissituation, peers access the network at different times. Thus, theavailable bandwidth of the network is dynamically changing with thedifferent mix of peers.

2. Bandwidth Heterogeneity and Bottleneck Effects. Even with a feasibleplayout rate r to satisfy Σ_(i∈V) _(t) C_(i)≧(|V_(t)|−1)r, another keyissue is to provide particular peers with satisfactory downloading rateso that they can play the video smoothly at the given rate r. As asimple example shown in FIG. 2(a), a video stream is pumped out from thesource node S to four participating nodes, A, B, C and D. In thisexample, the overall bandwidth available is 600 which should be plentyto simultaneously support the four users to play the video at the rateof 100. However, since peer C is overloaded, it becomes a bottleneckpeer in streaming. Accordingly, all the downstream nodes suffer with thethrottled downloading rates and are not able to play the video at thedesired rate. How can the same video be delivered by a different networkis as the example shown in FIG. 2(b). This network balances the uploadworkload of the four participating peers, where the bottleneck peers areeliminated and all the peers can now play the video smoothly. To fullyutilize the overall bandwidth for video delivery, the above two examplesshow the fundamental problem of how to accommodate the bandwidthheterogeneity of peers and eliminate the bottleneck effects from videostreaming.

3. Low Cost and Effective Adaptation in Highly Dynamic Systems. Withpeers frequently joining and departing from the network, anotherchallenging problem is how to continuously adapt the overlay network tomaintain the necessary condition and high bandwidth utilization at thesame time. Further, since a peer-to-peer streaming network normallyinvolves a huge number of peers online simultaneously, the constructionand adaptation of overlay must take place in a completely distributedmanner with low communication and operation overhead.

SUMMARY

Intelligent Peer-to-Peer Media Streaming

A framework and system of intelligent streaming live media contentsthrough end host cooperation is disclosed. The invented multimediacontent distribution system consists of one media source and a group ofpeers which dynamically changes with peer arrivals and departures. Thoseentities form and maintain an overlay network that converges to a globalload-balance state to achieve cooperative downloading. Contentdistribution in this overlay topology further employs an intelligenttopology formation algorithm, and an efficient media streaming protocol.

The advantages of the proposed approach are highlighted as follows:

The intelligent topology formation algorithm is a proactive approach tobuild a topology with tunable parameters to achieve various objectives.

The proposed approach achieves equal capacity per degree at each peer.

The proposed approach allows the media streams to flow smoothly acrossthe network and at the same time fully utilize the spare bandwidth ofeach peer.

The proposed approach is a hybrid approach to stream media contents withthe advantages of both tree and data-driven structures but without theirdrawbacks.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed inventions will be described with reference to theaccompanying drawings, which show important sample embodiments of theinvention and which are incorporated in the specification hereof byreference, wherein:

FIG. 1 shows an overview of the live video streaming system inaccordance with the preferred embodiment. The original raw content isfirst generated from the A/V source and then injected into the mediaserver for distribution. The individual user in the overlay networkdownloads the video content and plays the content at the playout rate rwhich is determined by the media server.

FIG. 2 shows an example of the overlay networks for video distribution,in which S is the media server, A, B, C and D are downloading nodes.FIG. 2(a) is an ill-connected network with peers A, B and D'sdownloading rates restricted to 50. FIG. 2(b) is the link-levelhomogeneous overlay network formed by our invention where all the linkshave the bandwidth 100.

FIG. 3 shows an illustrative example of the convergence of the capacityper fanout of nodes using our invention.

FIG. 4 shows an example of the trace of the walkers in the overlay. Thewalker is issued by peer x₀ and stops at peer y.

FIG. 5 shows the pseudocode of the invented distributed approach usingthe Metropolis-Hastings algorithm.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The numerous innovative teachings of the present application will bedescribed with particular reference to the presently preferredembodiment (by way of example, and not of limitation).

To address unpredictable and dynamically changing overall bandwidthproblem, the proposed approach dynamically adapts the playout rate intune with the changes of Σ_(i∈V) _(t) C_(i). Specifically, the preferredembodiment makes the playout rate r as a function of time denoted byr_(t), and enables the media source to adoptively control it based onthe overall bandwidth resource such that at any time snapshot t, Σ_(i∈V)_(t) C_(t)≈(|V_(t)|−1)r_(t). Consequently, if the overall bandwidth isnot plenty to support all the users with current playout rate, the mediaserver will decrease the playout rate r_(t) by further compressing thevideo content. On the other hand, if the overall bandwidth is plenty,the media server will generate high quality video streams with a higherplayout rate r_(t) so that all the peers will benefit from the improvedvideo quality. As a result, not only can the peer-to-peer streamingsystem adapt to the changes of the network resource without overloadingthe media server, but also peers can enjoy the best possible videoquality all the time.

To address the bandwidth heterogeneity and bottleneck effects problem,the disclosed approach forms an adaptive load-balance topology. Here,the number of out-going connections of peers is automatically adaptedbased on their upload bandwidth and the adequacy of the overallbandwidth. As a result, peers are balanced in uploading workload and allthe overlay connections converge to equal bandwidth to achieve a globalload-balance state as the link-level homogeneity. With no peersperforming as bottlenecks in this desirable state, full bandwidthutilization is achieved and peers can download at the satisfactory rate.

Meanwhile, in this link-level homogenous state, since the workload ofthe server is also adapted and related to Σ_(i∈V) _(t) C_(i), the servercan estimate the average downloading rate of peers and adaptively tunethe playout rate r_(t) by observing its own transmission workload only.

To achieve low cost and effective adaptation highly dynamic system, theproposed system is formed in the same spirit of the incremental update.Specifically when a node joins or departs from the network, only somelocal nodes affected by connection or disconnection update the localoverlay graph, whereas all the other nodes remain unchanged. Therefore,the adaptation of the overlay network involves only some affectedneighboring nodes without disturbing the other nodes. Moreover, theproposed system can converge fast to the desired link-level homogeneousstate and work efficiently in the high churning systems.

Incorporating with the overlay topology adaptation, the adaptation ofthe playout rate is open-loop. In other words, the server can timelytune the playout rate based on its local information only and no anyfeedback signals are required from the overlay networks.

The preferred embodiment of the present inventions and their advantagesare best understood by referring to FIGS. 1 through 5 of the drawings.Without loss of generality, the teaching considers one multicast session(or video channel) so that all the peers are watching or downloading thesame video content. However, the application does not limit to onemulticast session, and one skilled in the art should easily apply theteaching to multiple multicast sessions.

The Basic Structure of the Overlay Network

The teaching nodes the overlay network as a directed graph G_(t)={V_(t),E_(t)} at each time snapshot t. V_(t) denotes the set of peers,including the source node. Et denotes the set of overlay connections andE_(t) ⊂V_(t)×V_(t).

_(t) the set of peer i's parent nodes which upload video streams to peeri. And

_(i) denotes the set of peer i's child nodes which download videostreams from peer i. The in-degree of node i is denoted by I_(i)=|

_(t)|. The out-degree of node i is denoted by O_(i)=|

_(i)|C_(i) denotes the upload capacity of peer i. The media source isregarded as a normal peer which always stays in the network within-degree constantly equal to 0.

This teaching makes the following two assumptions.

First, random linear network coding is implemented in the system. Withnetwork coding, instead of forwarding the received video streamsdirectly, each peer sends the coded video streams which are the linearcombinations of its downloading video streams. The information is evenlyspread in the coded streams and is always useful to the receiving node.Therefore, each node is always able to retrieve the non-redundant videostreams from any selected parent nodes in the overlay. This assumptionenables the teaching to focus on constructing the overlay topology.However, with the generality of the achieved link-level homogeneousproperty, the disclosed approach can also be combined with other codingmethods such as Multiple Description coding and Fountain Code, as wellas the tree-based or block-oriented schemes to facilitate efficientvideo delivery.

Second, the teaching assumes that the transmission bottleneck is alwaysin the first hop of the uploading side, rather than inside the networkcore or on the downloading side. This is due to the widely adoption ofbroadband networks and asymmetric access links of users. With thisassumption, the teaching calculates the bandwidth of the out goingconnections of a peer i∈V_(t) as $\frac{C_{i}}{O_{i}}$and proposes to adapt peers' out-degrees proportional to their uploadbandwidth in the highly dynamic overlay. Finally, in the global network,$\frac{C_{i}}{O_{i}}$converges to a same constant i∈V_(t) and therefore all the overlayconnections aim to achieve the same bandwidth, i.e., link-levelhomogeneity.Construction of Overlay Backbone

The disclosed approach forms a link-level homogeneous overlay topologythat takes advantage of the upload bandwidth of all the participatingpeers as much as possible and evenly allocates the upload workload topeers. Various classes of embodiments are available. This teachingdiscusses two of them: one proposes a centralized at algorithm which canbe implemented in small size networks with hundreds of peers involvedand another is a completely distributed algorithm which is based uponthe main idea of the centralized algorithm for large-scale network.

Centralized Approach

This class of embodiment assumes that a central controller is available.The controller has the global information, including the addresses ofall the participating peers, their upload bandwidth values and theircurrent out-degrees.

Peer Joining When a peer, e.g., peer j, joins at t, to connect it to theoverlay graph, the controller sorts the existing peers in a descendingorder according to their capacity per out-degree values as$\left\{ {\frac{C_{i_{1}}}{O_{i_{1}}},\frac{C_{i_{2}}}{O_{i_{2}}},\ldots\quad,\frac{C_{i_{v_{t}}}}{O_{i_{v_{t}}}}} \right\}$where$\frac{C_{i_{1}}}{O_{i_{1}}} \geq \frac{C_{i_{2}}}{O_{i_{2}}} \geq \ldots \geq {\frac{C_{i_{V_{t}}}}{O_{i_{V_{t}}}}.}$For those nodes with out-degree equal to 0, we manually set theirout-degree values to be ε in computing the capacity per out-degree valuewhere ε is a constant and 0<ε≦1. After that, the controller will choosefrom the ordered list the first I_(j) peers which are$\left\{ {\frac{C_{i_{1}}}{O_{i_{1}}},\frac{C_{i_{2}}}{O_{i_{2}}},\ldots\quad,\frac{C_{i_{I_{j}}}}{O_{i_{I_{j}}}}} \right\}$as the parent nodes of peer j, where I_(j) is specified j peer. Afterpeer j is connected into the network its out-degree is 0 and itsdownloading rate can be up to the sum of the capacity per out-degreevalues of all its parent nodes.

Peer Rebuilding When a peer, e.g., peer j, departs from the network,each of its child nodes will lose a downloading link. Accordingly, thesechild nodes' downloading rates will decrease. To compensate for theloss, the server will rebuild one link for each of them. The rebuildingprocedure is similar to the joining procedure. With each rebuilding,only the peer currently with the largest capacity per out-degree valueis chosen to be connected as the parent node. Using the centralizedalgorithm, in the stable state where peers join and depart from thenetwork at the same rate, the capacity per out-degree values of all livenodes converge to the same equilibrium value δ_(C), i.e.,$\begin{matrix}{{{\delta_{C} = {\lim\limits_{t\rightarrow\infty}\frac{C_{i}}{O_{i}}}},{\forall{i \in V_{t}}}}{where}} & (1) \\{\delta_{C} = {E\left( \frac{\sum\limits_{i \in V_{t}}C_{i}}{\sum\limits_{i \in V_{t}}O_{i}} \right)}} & (2)\end{matrix}$

FIG. 3 illustrates a simple example. Suppose that the network currentlyhas 5 participating peers A, B, C, D and E with their capacity perout-degree values shown in FIG. 3. Suppose that peer j joins in thenetwork with I_(j)=2. In this case, A and D will be chosen as the parentnodes since they currently have the largest capacity per out-degreevalues. By adding one more out-going connection to peer j, both peers Aand D's capacity per out-degree values will be reduced accordingly. Onthe other hand, the capacity per out-degree value of peers B and E canalso increase when their child nodes depart. As peers continuously joinand depart, the capacity per out-degree values of all the peers willfinally converge to the same value δ_(C). Our simulations validated thiseffect.

Open-loop Playout Rate Adaptation By using the centralized algorithm,with each peer j∈V_(t), having I_(j) incoming connections and eachconnection downloading at the rate δC, the downloading rate of peer j istherefore δ_(C)I_(j). However, in order to ensure that all the users canplay the video smoothly, the playout rate r_(t) must be no larger thanthe smallest downloading rate of peers. In other words, peers with thesmallest in-degree will become the bottlenecks in terms of the playoutrate. For this reason, the preferred embodiment enforces that all thepeers have the same in-degree value, i.e., I_(j)=m, ∀j∈V_(t). In thiscase, from Eqn (2), all the peers cart have the same downloading rated_(C) as $\begin{matrix}{{d_{C} = {{\delta_{C}m} = {E\left( \frac{\sum\limits_{i \in V_{t}}C_{i}}{{V_{t}} - 1} \right)}}}{{{with}{\quad\quad}{\sum\limits_{i \in V_{t}}O_{i}}} = {{\sum\limits_{i \in V_{t}}I_{i}} = {\left( {{V_{t}} - 1} \right){m.}}}}} & (3)\end{matrix}$

Since the media source is also involved in the centralized algorithm,its capacity per out-degree value will also converge to the globalequilibrium δ_(C). Therefore, it can simply estimate the downloadingrate of peers by observing its own capacity per out-degree value at timet, denoted by δ_(t) ^(x), and multiplying this value by m. In this case,it can adaptively tune the playout rate with $\begin{matrix}{r_{t} = {{{\delta_{t}^{S}m} \approx d_{C}} = {E\left( \frac{\sum\limits_{i \in V_{t}}C_{i}}{{V_{t}} - 1} \right)}}} & (4)\end{matrix}$Distributed Approach

The disclosed approach forms a link-level homogeneous overlay topologydistributively. Here we use V to denote V_(t), η_(i) to denote C_(i),and k_(i) to denote O_(i). We assume that there is a central entitynamely Rendezvous Point (RP) to help new peers join the network, justlike the tracker in BitTorrent. The RP's IP address is public and knownby peers. Upon joining Into the network, each node, e.g., peer i willcontact the RP and fetch the peer list containing a set of nodes,denoted by V_(t).

Construction of Overlay Backbone

The topology formation algorithm is used to form the overlay backbone ofthe system on which the media contents are then streamed. The basic ideais: after being bootstrapped in the network by RP, the new peer willselect an active peer i in the network to connect with the targetprobability π(i), $\begin{matrix}{{\pi(i)} = \frac{\frac{\eta_{i}^{\alpha}}{k_{i}^{\beta}}}{\sum\limits_{j \in V}\frac{\eta_{j}^{\alpha}}{k_{j}^{\beta}}}} & (1)\end{matrix}$where α and β are used for “matching” a wide range of overlayapplications in terms of peer's workload. Roughly speaking, if α issmall and β is large, the node degree would increase slowly. If α islarge and β is small, then the node degree would increase fast. Theconcrete value of α and β can be turned adaptively according to thevarious QoS requirements in different systems, and more particularly thepreferred embodiment chooses α=2 and β=1 to achieve the link-levelhomogeneity where ∀i∈V such that $\frac{\eta_{i}}{k_{i}}$is a constant.

The new approach discloses how to connect the new arrivals to theappropriate peers following the target distribution in (1) via adecentralized manner. The main procedures are (a) joining procedure and(b) rebuilding procedure.

a) Joining Procedure

When a new peer, x, joins the network, it will randomly choose m_(x)peers in V_(x) and issue one walker to each of them. These randomwalkers will be passed among peers based on the invented algorithm. Eachwalker is assigned a time-to-live (TTL) value, r. The walker isforwarded from the current node to a neighboring note based on theproposed edge transition probability and the walker's TTL is decrementedby one after each forwarding. The new node connects to a node at whichthe walker stops (i.e. TTL=0). If a walker stops at the node which isalready connected by that new node, then the walker moves additionalsteps, e.g., one step. FIG. 4 shows an example of the process andAlgorithm 1 in FIG. 5 presents details. Next, the designing philosophyof the edge transition probability P(j, i), i.e., the probability thatthe walker is forwarded to peer j by peer i, is discussed.

The transition probability, P(j, i), with which the walker is nowstaying at peer t and will be forwarded to peer j∈V_(t), isP(j, i)=q(j, i)·α(j, i)where q(j ,i) i is called selection probability, i.e., the probabilitypeer j is selected by peer i from its neighbor list as the candidate toforward the walker. α(j, i) is called acceptance probability, i.e., theprobability that candidate j will finally be accepted as the nextstation to receive the walker. The selection probability is flexible andcan be proposed differently according to the actual networkenvironments. One proposal is, $\begin{matrix}{{q\left( {j,i} \right)} = \frac{1}{{\overset{\sim}{k}}_{i} + 1}} & (3)\end{matrix}$where {tilde over (k)}_(i)=|V_(i)|.

The acceptance probability α(j, i) is defined as, $\begin{matrix}\begin{matrix}{{\alpha\left( {j,i} \right)} = {\min\left\{ {\frac{{\pi(j)} \cdot {q\left( {i,j} \right)}}{{\pi(i)} \cdot {q\left( {j,i} \right)}},1} \right\}}} \\{= {\min\left\{ {{{\left( \frac{\eta_{j}}{\eta_{i}} \right)^{\alpha} \cdot \left( \frac{k_{i}}{k_{j}} \right)^{\beta}}\frac{{\overset{\sim}{k}}_{i} + 1}{{\overset{\sim}{k}}_{j} + 1}},1} \right\}}}\end{matrix} & (4)\end{matrix}$

With such a definition of α(j, i), a superior neighbor j with moreavailable resource j (e.g., uplink bandwidth) and smaller degree {tildeover (k)}_(j) will be chosen with algorithm with the ability to probedeep in the network and dig out these hidden matching peers. The later,however, will inevitably be trapped at a local optimal peer. Theintegrated transition probability P(j, i) defined in (2) is then,$\begin{matrix}{{P\left( {j,i} \right)} = \left\{ \begin{matrix}{{\frac{1}{{\overset{\sim}{k}}_{i} + 1}\min\left\{ {{{\left( \frac{\eta_{j}}{\eta_{i}} \right)^{\alpha} \cdot \left( \frac{k_{i}}{k_{j}} \right)^{\beta}}\frac{{\overset{\sim}{k}}_{i} + 1}{{\overset{\sim}{k}}_{j} + 1}},1} \right\}},} & {{i \neq j},{j \in V_{i}},} \\{{1 - {\sum\limits_{{j^{\prime} \in V_{i}},{j^{\prime} \neq i}}{P\left( {j^{\prime},i} \right)}}},} & {j = {i.}}\end{matrix} \right.} & \begin{matrix}\left( {5a} \right) \\\quad \\\quad \\\left( {5b} \right)\end{matrix}\end{matrix}$

The preferred embodiment has adopted a sampling method to choosematching peers with the so called transition probability, the larger thesample set, i.e., {tilde over (k)}_(i), is at each stop i; the betterthe algorithm will perform. The perferred embodiment adopts 20≦{tildeover (k)}_(t)≦50. To achieve this, each peer i can fetch the peer listV_(t) from the RP as soon as its {tilde over (k)}_(i) is smaller than20.

b) Rebuilding Procedure

To fight the churn of peers, it is necessary to repair the constructedtopology to prevent network breakdown and node isolation. The repairingprocess is called rebuilding procedure, which can be done with followingrebuilding schemes.

Rebuilding Schemes

When a node leaves the network, all of its neighbors lose a link. Thereactive rebuilding scheme is invoked passively only when a peer i losta link. To guarantee their connectedness, those nodes which lost a linkwill then rebuild a new link(s) with probability r_(i) to compensate forthe lost one. When a node i tries to rebuild a link, it issues a walkerwith a TTL value, τ, to one of the peers in V_(i). The walker thentraverses the network, same as that in the node joining process.Finally, a new link is created by connecting node i and the node atwhich the walker stops. Three representative rebuilding process schemesare described here (in the following, k_(t) 's denotes the degree ofnode i just after losing a link).

Always Rebuilding Scheme: The nodes always rebuild one link tocompensate for each lost link, i.e. r_(i)=1 for every node i.

Probabilistic Rebuilding Scheme: The nodes rebuild a link based on aprobability r, Mathematically $\begin{matrix}{r_{i} = \left\{ \begin{matrix}{1,} & {k_{i}^{\prime} = 2} \\{r,} & {k_{i}^{\prime} \geq 3}\end{matrix} \right.} & \begin{matrix}\left( {6a} \right) \\\left( {6b} \right)\end{matrix}\end{matrix}$for every node i. The threshold on k_(i)′=3 means that each node has tomaintain at least three links to ensure network connectedness.

Adaptive Rebuilding Scheme: The nodes gradually rebuild links when theirdegrees are getting large in order to prevent overloading. At the sametime, each node i should maintain at least m, links such that theoverlay service performance and reliability are not degraded. Therefore,this rebuilding process allows the nodes to make rebuilding decisionadaptive by considering their current degrees.Mathematically, $\begin{matrix}{r_{i} = {\min\left\{ {\frac{m_{i} - 1}{k_{i}},1} \right\}}} & (7)\end{matrix}$for each node i.

Link Level Homogeneity

The term “Link Level Homogeneity” refers to the property that eachvirtual link is of the same service power. More specifically, in a linklevel homogeneous network G, ∀i∈V, there is${\frac{\eta_{i}}{k_{i}} = \delta_{C}},$where δ_(C) is $\begin{matrix}{\delta_{c} = \sqrt{\frac{\mu\quad Z}{\lambda\quad m{V}}}} & (10)\end{matrix}$where ${Z = {\sum\limits_{i \in V}\frac{\eta_{i}^{2}}{k_{i}}}},$λ is the mean arrival rate of peers and μ is the mean departure rate ofpeers. (10) is achieved without considering the rebuilding procedure. Anobvious advantage of the so-called link level homogeneity is that peersin the network are interconnected with media pipes of equal bandwidth.Media streams conducted on these pipes can thus flow smoothly andwell-proportioned and will not encounter bottlenecks. In such ascenario, peers at the downstream will not suffer from the poor servicecapability of the upstream peers and how to organize the heterogeneouspeers is thus solved perfectly. What is more, thanks to our proactivetopology formation algorithm, once a peer is connected to the network, apeer can achieve matched service from others and therefore do not needto modify its local topology to accommodate the heterogeneous networkany more. Since these reactive modifications will not only degrade itsown video quality, but also its children's, by avoiding the churningprocedure, our system is thus more steady-going and suitable forrigorous QoS requirements.Distributed Call Admission Control

In the previous presentation, we assume that the video playout rater_(t) can be adaptively tuned based on the adequacy of the overallbandwidth resource in the system. However, in some applications, ifr_(t) can not be turned, we further propose a distributed call admissioncontrol mechanism so that the Necessary Condition of a feasible networkis always satisfied. This distributed call admission control mechanismcan be implemented in either the centralized topology formation approachor the distributed approach.

Specifically, using our mechanism, downloading rate of peers, d_(C), canbe estimated by each individual peer, just like the server usingequations (3) and (4). In this case, in order to meet the NecessaryCondition, we must guarantee that the estimated downloading rate ofpeers is larger than the fixed playout rate. In a nutshell, using thedistributed call admission control mechanism, by estimating the adequacywith local information, each node makes the decision locally whether toaccept and serve the new peers or not.

For example, suppose that a node j joins the network and selects peer ias its parent nodes, where i can be selected using either the presentedcentralized approach or distributed approach. To make the decisionwhether to serve peer j or not, peer i first estimates its own capacityper fanout at current time, denoted as δ_(t) ^(i). With δ_(t) ^(i), peeri can estimate the adequacy of the overall bandwidth resource bycomputing the expected downloading rate of peers as d_(c)=mδ_(t) ^(i).With this information, if d_(C)≦ζr, peer i will admit peer j C_(j)≧r andreject it otherwise. On the other hand, d_(C)>ζr, peer i will alwaysadmit peer j regardless peer j's uploading bandwidth. ζ is predefinedand used to control the robustness of the system. If ζ small, the systemis greedy to involve peers in the system but it may hurt the downloadingperformance of peers. If ζ is too large, the system is conservative andwill reject peers even there is abundant resource available in thesystem.

According to various disclosed embodiments, there is provided: A methodof distributing high-bandwidth content over a peer-to-peer network,comprising: when a peer joins the network, launching a walker procedurewhich explores the current network topology to find exiting nodes whichcan provide the best connectivity to the peer; and connecting the newpeer to at least one tree within the network, in dependence on theresults of said walker procedure, to thereby receive content streams.

According to various disclosed embodiments, there is provided: A methodof distributing high-bandwidth content over a peer-to-peer network,comprising; when a peer joins the network launching a walker procedurewhich explores the current network topology to find exiting nodes whichcan provide the best connectivity to the peer, individual substreamswhich in combination provide a single high-bandwidth data stream; andconnecting the new peer to at least one tree within the network, independence on the results of said walker procedure, to thereby receivecontent.

According to various disclosed embodiments, there is provided: Apeer-to-peer network method for multi-media distribution, comprising theactions of: monitoring the overall bandwidth resource availability;dynamically adapting playout rate at the media source in dependence ofsaid overall bandwidth; and forming and maintaining an adaptiveload-balancing topology such that the number of outgoing connections ofones of peers in the network is automatically adapted in dependence ofits upload bandwidth and said over-all bandwidth.

According to various disclosed embodiments, there is provided: Apeer-to-peer network method for multi-media distribution, comprising theactions of: supplying a playout stream at a media source; forming andmaintaining an adaptive load-balancing topology such that the number ofoutgoing connections of ones of peers in the network is automaticallyadapted in dependence on its upload bandwidth and said overallbandwidth, while locally applying call admission criteria which avoidnetwork degradation.

According to various disclosed embodiments, there is provided: A networkarchitecture for peer-to-peer communications, comprising: a media sourcewhich plays out media content at an adaptive rate in dependence of itscapacity per out-degree value; and a peer-to-peer network comprisingsaid media source as a peer and at least one other peer; wherein ones ofsaid peers connect to other said peers for downloading said content; andsaid network dynamically selects a certain said peer with preference independence on capacity per out-degree values to connect a new peer fordownloading and updates connections locally in response to at least someevents; whereby the capacity per out-degree values of all said peers insaid network converge to substantial uniformity.

According to various disclosed embodiments, there is provided: A networkarchitecture for peer-to-peer communications, comprising: a media sourceas a peer; and at least one other peer; an overlay network where saidmedia source plays out media content at an adaptive rate in dependenceof its capacity per out-degree value and ones of said peers but not saidmedia source connects to other said peers for downloading said contentand the capacity per out-degree values of all said peers in said networkconverge to a substantially same value; and a network comprising allsaid peers including said media source; wherein ones of connections insaid network are constructed in dependence on transition probability,and out degrees of said peers are larger than one value X but smallerthan another value Y; and a new peer joins said network by randomlyconnecting to a medium number, between said X and Y, of said peers, andjoins said overlay network by propagating requests through said networkin searching, in dependence on said transitional probability, for peerswith preferred capacity per out-degree values to connect to.

According to various disclosed embodiments, there is provided: A networkarchitecture for peer-to-peer communications, comprising: a media sourcewhich plays out media content into a peer-to-peer network, said networkincluding peers which connect to other said peers for downloading saidcontent; and local node selection procedures which assign data downloadsources to new nodes in at least partial dependence old capacity perout-degree values.

According to various disclosed embodiments, there is provided: A networkarchitecture for peer-to-peer communications, comprising: a media sourcewhich plays out media content into a peer-to-peer network said networkincluding peers which connect to other said peers for downloading saidcontent; and a walker procedure, locally executable by nodes of thenetwork, which explores the current network topology to find existingnodes which can provide the best connectivity to a newly joined orrejoined peer.

Modifications and Variations

As will be recognized by those skilled in the art, the innovativeconcepts described in the present application can be modified and variedover a tremendous range of applications, and accordingly the scope ofpatented subject matter is not limited by any of the specific exemplaryteachings given. Many variations are noted above, and many others arepossible.

For example, in some embodiments the transitions of the walker can bedeterministic rather than stochastic. An in stance of this would be awalker which transitions only to the first-found adjacent node havingthe highest fanout-scaled available bandwidth.

For example, in some embodiments the transitions of the walker can bedeterministic rather than stochastic. An instance of this would be awalker which transitions only to the first-found adjacent node havingthe highest fanout-scaled available bandwidth.

It should be noted that the node evaluation criteria used by the walkerdo not have to be precisely accurate measurements of node optimality,since the overall process has enough stochasticity to achieve goodoptimization anyway.

For another example, a variety of rules can be used for stopping thewalker procedure. One simple rule is to allow each walker a fixed numberof transitions, but another is to stop when a walker has made some fixednumber of transitions without improving on the best node previouslyfound.

For another example, in some embodiments the walker procedure can beavoided, by using a centralized approach as described above.

For another example, a variety of rules can be used to guaranteetransition of the walker procedure from node to node without stalling orspawning.

For another example, initial nodes for launch of the walker procedurescan be launched in a variety of ways, including random selection orotherwise.

None of the description in the present application should be read asimplying that any particular element, step, or function is an essentialelement which must be included in the claim scope: THE SCOPE OF PATENTEDSUBJECT MATTER IS DEFINED ONLY BY THE ALLOWED CLAIMS. Moreover, none ofthese claims are intended to invoke paragraph six of 35 USC section 112unless the exact words “means for” are followed by a participle.

The claims as filed art intended to be as comprehensive as possible, andNO subject matter is intentionally relinquished, dedicated, orabandoned.

1. A method of distributing high-bandwidth content over a peer-to-peernetwork, comprising: when a peer joins the network, launching a walkerprocedure which explores the current network topology to find existingnodes which can provide the best connectivity to the peel; andconnecting the new peer to at least one tree within the network, independence on the results of said walker procedure, to thereby receivecontent streams.
 2. The method of claim 1, wherein said walker procedureuses transitions which are at least partly stochastic.
 3. The method ofclaim 1, wherein said walker procedure makes transitions in dependenceon available bandwidth per fallout.
 4. The method of claim .1, whereinsaid walker procedure makes transitions to nodes with successivelyhigher available bandwidth per fanout.
 5. The method of claim 1, whereinsaid walker procedure makes transitions in accordance with a.Metropolis-Hastings algorithm.
 6. The method of claim 1, wherein saidconnecting step is conditioned on distributed call admission.
 7. Themethod of claim 1, wherein said connecting step is not permitted if thenew peer's uploading bandwidth is smaller than the playout rate and thelocally estimated downloading rate.
 8. A method of distributinghigh-bandwidth content over a peer-to-peer network, comprising: when apeer joins the network, launching a walker procedure which explores thecurrent network topology to find existing nodes which can provide thebest connectivity to the peer, for individual substreams which incombination provide a single high-bandwidth data. stream; and connectingthe new peer to at least one tree within the network, in dependence onthe results of said walker procedure, to thereby receive content.
 9. Themethod of claim 8, wherein said walker procedure uses transitions whichare at least. partly stochastic.
 10. The method of claim 8, whereinsaid. walker procedure makes transitions in dependence on availablebandwidth per fanaut.
 11. The method of claim 8, wherein said walkerprocedure makes transitions to nodes with successively higher availablebandwidth per fanout.
 12. The method of claim 8, wherein said walkerprocedure makes transitions in accordance with a Metropolis-Hastingsalgorithm.
 13. The method of claim 8, wherein said connecting step isconditioned on distributed call admission.
 14. The method of claim 8,wherein said connecting step is not permitted if the new peer'suploading bandwidth is smaller than the playout rate and the locallyestimated downloading rate.
 15. A peer-to-peer network method formulti-media distribution, comprising the actions of: monitoring theoverall bandwidth resource availability; dynamically adapting playoutrate at the media source in dependence of said overall bandwidth; andforming and maintaining an adaptive load-balancing topology such thatthe number of outgoing connections of ones of peers in the network′. isautomatically adapted in. dependence of its upload bandwidth. and saidoverall bandwidth.
 16. The method of claim. 15, wherein said adaptiveload-balancing topology uses stochastic exploration of the network whena new node joins.
 17. The method of claim 15, wherein the media sourceprovides a playout of streaming video. 18-32. (canceled)
 33. A networkarchitecture for peer-to-peer communications, comprising: a media sourcewhich plays out media content into a peer-to-peer network, said networkincluding peers which connect to other said peers for downloading saidcontent; and a walker procedure, locally executable by nodes of thenetwork, which explores the current network topology to find existingnodes which can provide the best connectivity to a newly joined orrejoined peer.
 34. The system of claim. 33, wherein said media contentcomprises streaming video.
 35. The system of claim 33, wherein saidwalker procedure uses transitions which are at: least partly stochastic.36. The system of claim 33, wherein said walker procedure makestransitions in dependence on available bandwidth per fanout.
 37. Thesystem of claim 33, wherein said walker procedure makes transitions tonodes with successively higher available bandwidth per fanout.
 38. Thesystem of claim 33, wherein said walker procedure makes transitions inaccordance with a Metropolis-Hastings algorithm.